Performance prediction is essential for energy-efficient computing in heterogeneous computing systems that integrate CPUs and GPUs. However, traditional performance modeling methods often rely on exhaustive offline profiling, which becomes impractical due to the large setting space and the high cost of profiling large-scale applications. In this paper, we present OPEN, a framework consists of offline and online phases. The offline phase involves building a performance predictor and constructing an initial dense matrix. In the online phase, OPEN performs lightweight online profiling, and leverages the performance predictor with collaborative filtering to make performance prediction. We evaluate OPEN on multiple heterogeneous systems, including those equipped with A100 and A30 GPUs. Results show that OPEN achieves prediction accuracy up to 98.29\%. This demonstrates that OPEN effectively reduces profiling cost while maintaining high accuracy, making it practical for power-aware performance modeling in modern HPC environments. Overall, OPEN provides a lightweight solution for performance prediction under power constraints, enabling better runtime decisions in power-aware computing environments.

翻译：性能预测对于集成CPU和GPU的异构计算系统中的能效计算至关重要。然而，传统性能建模方法往往依赖详尽的离线性能剖析，这在大型设置空间和大规模应用剖析成本高昂的情况下变得不切实际。本文提出OPEN框架，该框架包含离线与在线两个阶段。离线阶段包括构建性能预测器并生成初始稠密矩阵。在线阶段中，OPEN执行轻量级在线剖析，并利用协同过滤技术结合性能预测器进行性能预测。我们在多个异构系统（包括配备A100和A30 GPU的系统）上评估了OPEN。结果表明，OPEN的预测准确率最高可达98.29%。这证明OPEN在保持高精度的同时有效降低了剖析成本，使其适用于现代高性能计算环境中功耗感知的性能建模。总体而言，OPEN提供了功耗约束下性能预测的轻量级解决方案，能够在功耗感知计算环境中实现更优的运行时决策。